As a venture capitalist, you have to evaluate hundreds of financial statements from potential portfolio companies. But how can you avoid getting lost in the numbers and focus on the key metrics that matter? In this article, we'll share some tips and tools to help you streamline your analysis and make better decisions.
Before diving into the data, you need to understand the context of the company you're analyzing. What is their business model, market, stage, and growth potential? How do they compare to their peers and competitors? What are their main challenges and opportunities? By answering these questions, you can narrow down the scope of your analysis and identify the most relevant indicators for their performance and valuation.
Look first for evidence that executive management has created and communicated a strategy and a tactical plan, and that progress in that plan can be measured with financial and operational data. Then look for evidence that collection and reporting of that data has been standardized, digitized, and automated.
Streamlining financial analysis begins by grounding your approach in a thorough understanding of the company's business model, market presence, and growth potential. Set clear objectives that guide you towards the most relevant data. Focus on key financial indicators such as revenue growth, profit margins, and liquidity ratios that align with your analysis goals. Don't overlook the importance of non-financial indicators like customer satisfaction/ NPS, which can significantly impact financial performance. Stay informed on market trends and external factors that could influence the company's trajectory. By focusing on these strategies, you can cut through the noise and make informed decisions, enhancing your financial analysis process.
I have discovered that concentrating on essential financial metrics like net cash flow, net profit margin, and revenue proves more beneficial in a valuation context, as opposed to fixating on metrics such as EBITDA, TCV, etc. It's advisable to steer clear of data that may be extrapolated to inflate valuation.
In finance, context isn't just king; it's the entire kingdom. Without it, data analysis is like navigating a labyrinth blindfolded. It's more than crunching numbers; it's about discerning stories hidden in those numbers. Take tech: innovation cycles aren't just patterns, they're lifelines that dictate revenue ebbs and flows. In real estate, ignoring seasonality in mortgage demands is like forecasting weather without considering seasons. Each industry speaks a different financial language. Misinterpretation leads to misguided strategies. Contextual intelligence is not just a tool, it's the cornerstone of astute financial decision-making and forecasting. It’s about seeing beyond the horizon, not just staring at the road.
I like other contributions a lot. Visualizing data, using various analysis techniques and understanding context are all excellent suggestions. I have a slightly different perspective, which is that I like to dive deep into the assumptions and reasoning behind financial data. What is known, what is unknown? Where are the key risks to a financial or valuation model? How are these assumptions reflected in the numbers? I want to know how a financial or valuation model is built to uncover the thinking behind the numbers before starting to analyze anything.
One way to simplify your analysis is to use standardized templates for financial statements. These templates can help you organize the data in a consistent and clear format, as well as highlight the key ratios and metrics that you need to evaluate. For example, you can use the Venture Capital Method template to estimate the pre-money and post-money valuation of a company based on its expected cash flows and exit multiple. You can also use the DuPont Analysis template to break down the return on equity (ROE) of a company into its profitability, efficiency, and leverage components.
You can use templates to speed up the process and to guarantee you are covering all of the main metrics. Be careful to always use a template that you are familar with, or that you had reviewed and you are sure that you understand all the calculations and assumptions included in the model. Analyse the behavior of the model and the sensibility to the main assumptions. A template can be a great shortcut, but a template that is not behaving as expected can be a huge risk for your analysis. Be sure that you understand the meaning of the outputs the template is providing. In my experience, each business will have some particularities that will have to be adapted or included. Even two companies in the same industry may have important differences.
Consider Netflix's Q3 2023 earnings release. Analyzing its financial statements through the lens of the Discounted Cash Flow (DCF) Valuation Method template, a standardized framework that estimates a company's intrinsic value based on its future cash flows, would have provided valuable insights into its long-term growth potential. By applying pre-defined assumptions and calculations within the template, investors could have gained a clearer understanding of Netflix's future profitability and made informed decisions about its stock price. Oversimplification: Templates can mask underlying complexities or nuances within the data. Misapplication: Applying the wrong template can lead to misleading conclusions.
???? Simplifiez votre analyse avec des modèles standardisés ! ???? Utilisez des modèles comme la Méthode du capital-risque pour estimer la valorisation ou le modèle DuPont pour ventiler le rendement des capitaux propres. Ces outils organisent les données de manière claire et mettent en évidence les ratios clés, simplifiant ainsi votre processus d'analyse financière. ????
Ditch the bespoke, convoluted financial models. Standardized templates aren’t just time-savers; they’re clarity providers. Templates like the Venture Capital Method or DuPont Analysis offer a structured approach, ensuring you’re not missing the forest for the trees. These aren't just tools for organization; they’re frameworks that guide your analytical thought process. Standardization doesn’t mean losing depth; it means analyzing with purpose and direction.
Pour répondre à la question, il est important d'identifier les éléments spécifiques à votre société et votre activité et c'est primordial. Ensuite, il faut voir qui est en charge d'analyser ses éléments, car il est évident que le dirigeant n'a pas la même "lecture" que le directeur financier, que le directeur commercial, etc... et chacun a donc besoin d'informations propres, mais standardisées. N'allez pas chercher des choses complexes, des choses pratiques, accessibles et que l'on peut suivre dans le temps.
Another way to streamline your analysis is to automate the calculations and formulas that you use frequently. This can save you time and reduce errors, as well as allow you to update your analysis with new data easily. You can use various tools and software to automate your calculations, such as Excel, Google Sheets, or online calculators. For example, you can use the Internal Rate of Return (IRR) calculator to estimate the annualized return of an investment based on its cash flows. You can also use the Sensitivity Analysis tool to test how different variables affect your assumptions and outcomes.
Automating the calculations is fundamental. In any projection, you will be looking into the future, and therefore considering many scenarios and assumptions. You will have at least a base, worst, and best case. I like to work with a "survival case": if everything goes wrong, will the company survive? Also fundamental to me is a sensitivity analysis. What are the main levers of the business? Which ones represent the main risks? Which ones have an important impact in the results and may have been intentionally biased towards better results? But for me the best use of automation is the possibility of conducting a meeting with the management team and building scenarios on the fly, capturing their inputs and perceptions to calibrate the model!
Beyond streamlining existing tasks, automation empowers us to generate unique insights. For instance, by automating sensitivity analysis across various financial models, we can identify key drivers of profitability and uncover potential risks for companies. This deductive reasoning, facilitated by automated tools, allows us to move beyond surface-level interpretations and make data-driven strategic decisions. Overreliance on technology: Automation can't replace critical thinking and the ability to interpret data. Black box effect: Complex automation can lack transparency, making it difficult to understand the underlying calculations.
???? Automatisez vos calculs pour une analyse efficace ! ???? Gagnez du temps et réduisez les erreurs en automatisant vos calculs financiers fréquents. Utilisez des outils comme Excel, Google Sheets ou des calculatrices en ligne pour cette tache. Par exemple, la calculatrice de taux de rendement interne (TRI) peut estimer le rendement annualisé d'un investissement en fonction de ses flux de trésorerie. De même, l'outil d'analyse de sensibilité vous permet de tester l'impact de différentes variables sur vos hypothèses et résultats. En automatisant vos calculs, vous pouvez mettre à jour facilement votre analyse avec de nouvelles données et prendre des décisions plus éclairées. ????
Manual calculations in today’s data-driven world? Unthinkable. Automation is not a convenience; it’s a necessity. But beware of automation complacency. Tools like Excel or Google Sheets are powerful, but garbage in is garbage out. Automate the mundane, but remain vigilant. Ensure the inputs are as robust as your automated outputs. This isn’t just about efficiency; it’s about reliability and accuracy in your analytical arsenal.
Une fois les choix faits, mettre en place un fichier Excel par exemple que vous pouvez partager, et ainsi vous aurez une feuille de calcul que vous pourrez utiliser avec des formules simples, lisibles. Pensez à toujours mettre des chiffres, des pourcentages et un graphique :-)
A picture is worth a thousand words, especially when it comes to financial data. Visualizing the data can help you spot trends, patterns, and outliers, as well as communicate your findings and recommendations more effectively. You can use various tools and software to visualize your data, such as charts, graphs, dashboards, or infographics. For example, you can use the Cash Flow Statement chart to show how a company generates and spends its cash over time. You can also use the Revenue Breakdown graph to show how a company diversifies its income sources and segments its customers.
One thing I learned a long time ago is that "people are different". You may be great at looking at a spreadsheet full of numbers, but some people don't. To build a robust model, you will have to interact with people form different backgrounds, different areas of the company, and particularly different mental models. Your goal is to communicate in the best possible way with everyone and capture their inputs, their perceptions, and their validation of the model. It will be much easier to go on with your project and you will avoid the risk of someone pointing out some (real or perceived) flaw in the model, just because it was not taken care properly earlier.
Visualizing data is not just for aesthetic appeal; it’s a critical analytical tool. Charts and graphs transform numbers into stories, making complex data digestible. Use visualization not as an afterthought but as a key component of your analysis. But remember, poor visualization can mislead. Each graph or chart should be a window into the data, not a distorted mirror. Use visualization to reveal truths, not to hide complexities.
Consider the recent economic uncertainty in 2023. Analyzing key economic indicators like inflation and unemployment rates through interactive dashboards would have allowed economists to visualize the evolving trends and their impact on various sectors. By comparing historical data alongside real-time updates, policymakers could have identified regions needing intervention and made informed decisions to mitigate the economic downturn. Beyond simply creating visuals, data visualization empowers us to generate unique insights. For instance, visualizing the correlation between inflation and consumer spending trends could reveal unexpected dynamics not evident solely from numerical analysis.
People may disagree, but I strongly believe that you can only visualize what you are already at least anticipating. Let me explain this: if you are just throwing random numbers into X and Y axis, that has nothing to do with analytics. But if you anticipate that one key driver of the business model could be for example "returning customer rate", then visualizing this and finding out about trends and correlations becomes powerful. Therefore you can only visualize what you understand.
???? Visualisez vos données financières pour des insights clairs ! ???? Utilisez des outils comme des graphiques, des tableaux de bord et des infographies pour repérer les tendances et communiquer efficacement vos conclusions. Par exemple, un graphique de flux de trésorerie montre comment une entreprise gère ses finances, tandis qu'un graphique de répartition des revenus illustre la diversification des sources de revenus. En visualisant vos données, vous facilitez la compréhension et la prise de décision. ????????
Finally, the most important part of your analysis is to ask the right questions. Questions can help you challenge your assumptions, validate your hypotheses, and uncover hidden insights. You should ask questions that are relevant, specific, and actionable, as well as questions that explore different scenarios and alternatives. For example, you can ask questions like: How scalable is the company's business model? What are the main risks and uncertainties facing the company? How does the company measure and improve its unit economics? What are the best and worst case scenarios for the company's growth and exit?
In each industry, some factors are more crucial and have more impact on final results. For example, in a certain industry, the winner is the one who can procure materials less, no matter how much they manufacture productively (yes, I know I am exaggerating!). If I am faced with lots of data, I would ask domain experts to identify what are the KEY influential factors of that industry. Thanks to the 80/20 rule, I anticipate that only 20℅ of the data affect 80℅ of the final results. So my first action as an overwhelmed expert is to find these 20℅ (more or less) data. This is a matter of asking the right question.
The art of asking good questions is based on building and testing the consistency of the model. Is the rate of sales growth consistent with market size, current market share and competitor's positioning and future actions? (yes, you have to dig into what your competitor is capable of, and not what he is doing now with his current capability and technology). Are investments and increase in costs consistent to support the expected sales growth? When do we reach production capacity or the limit of capacity to provide the service? Is the ramp-up schedule reasonable or too aggressive? Are we too optimistic in our assumptions? The more difficult questions we ask, the more robust the model will be.
Consider the recent acquisition of Twitter by Elon Musk in 2023. Asking pointed questions like "How will the new monetization strategy affect user engagement?" or "Can Twitter maintain its brand identity under Musk's leadership?" would have empowered analysts to assess the deal's potential impact and make informed investment decisions. This proactive questioning, beyond simply analyzing historical data, provided valuable insights into the company's uncertain future. Analyzing Twitter's user demographics alongside potential changes to the platform's content moderation policies could reveal unforeseen risks or opportunities.
Asking the right questions involves inquiries about various risks, such as reputational, key personnel loss, compliance, legal, sanitary risks…
Too much of the time we can get caught up with what is easy to measure without asking if it is what is truly driving the business. It’s akin to the drunk looking for his keys under the streetlight. When asked if that was where he lost them he responds, “No, but this is where the light is.” Just because you can measure something does not mean you should. What are the key drivers of YOUR business? Track those daily. Have a scoreboard the entire team can see and be clear on your goals and how you are tracking. Perhaps capture the others (it is difficult to create data after the fact), but make sure you are clear, and you are clear with you team on the truly “KEY” Performance Indicators for your business at this time.
To achieve high impact on business focus on key metrics, industry benchmarks, and future trends. Scrutinize cost structures, revenue streams, and customer behavior. It is recommended to leverage advanced analytics for actionable insights. This approach enables informed decisions for sustained success.
Automate the collection - focus on interpretation. At Valor we automate portfolio data collection. That frees up time to spend on analyzing. In to the initial point of the conversation - while we have the full P&L / Balance sheet we focus on a subset and only zoom in when the situation requires it.
Well, it's funny how naive it is to depend on templates and automated tools for financial analysis. It's true that they save time, but the real value is in knowing how markets work and how technology changes, not in having well-organized data. Do the math automatically? That could lead to an excellent analysis, but it leaves out the deep observations. And making facts visible? A pretty map is just that—beautiful. Things that really matter are the depth of understanding and the ability to see past numbers and charts. Let's not hide behind lists and graphs. Real financial smarts mean questioning what you think you know and being willing to think outside the box. That's what startup investing is all about.
Prioritize Data Quality: Ensure the data you're analyzing is accurate and up-to-date. Poor quality data can lead to misguided decisions. Collaborate and Share Insights: Engage with the community or your team to share insights and perspectives. Collaboration can unveil new angles and opportunities. Keep Learning: Stay updated with the latest tools, trends, and analysis techniques. The world of finance is dynamic, and continuous learning will enhance your decision-making capabilities. Focus on Decision Impact: Always align your analysis with the potential impact on decisions. Concentrate on data that offers the most significant insights for action.
1. Adaptabilité : Comment l’entreprise s’ajuste-t-elle face aux retours et aux évolutions du marché ? 2. Acquisition et rétention : Quelles stratégies sont en place pour attirer et garder les clients, et quel est leur co?t ? 3. Partenariats : Existence de collaborations stratégiques pour stimuler la croissance ? 4. Innovation : Quelle est la part de technologie ou d’innovation qui sécurise un avantage concurrentiel ? 5. Impact : Quel est l’impact social ou environnemental visé ? 6. Vision à long terme : Quels sont les objectifs à long terme et les plans d’évolution ?